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In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost‐saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013–2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway‐Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100‐car trains is associated with 1.11 (95% confidence interval: 1.10–1.12) times the derailment odds of running 50‐car trains (or a 11% increase), even accounting for the fact that only half as many 100‐car trains would need to run. For 200‐car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20–1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding. [ABSTRACT FROM AUTHOR]

This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called "adhesion") as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves' transpiration characteristics while ensuring the result's reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present. [ABSTRACT FROM AUTHOR]

The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers. [ABSTRACT FROM AUTHOR]

Featured Application: In this study, the impact of errors and missing information in the inventory data of highway–rail grade crossings (HRGCs) on the crash frequency prediction models currently used in the US is investigated. Transportation authorities and safety regulators can apply the insights gained from this research to enhance the accuracy of crash frequency prediction models for HRGCs. By utilizing more accurate and complete inventory data, these models provide more reliable crash frequency predictions, enabling better resource allocation and more targeted safety interventions. The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration's (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA's 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and secondly, utilizing a field-validated inventory dataset for the same 560 HRGCs as input to the FRA's 2020 Accident Prediction (AP) model. The findings indicated a significant statistical disparity in the predictions made with the two input datasets. Furthermore, two new Zero-inflated Negative Binomial (ZINB) models were estimated by employing 5-year reported HRGCs crashes and the two inventory datasets for the 560 HRGCs. These models facilitated the comparison of model parameter estimates and estimated marginal values. The results indicated that errors and missing values in the original FRA HRGCs inventory dataset resulted in crash predictions that statistically differed from those made using the more accurate and complete (validated in the field) HRGCs inventory dataset. Furthermore, the crash prediction model estimated upon the corrected inventory data demonstrated enhanced prediction performance, as measured by the statistical fitness criteria. The findings emphasize the importance of collecting complete and accurate inventory data when developing HRGCs crash frequency models. This will enhance models' precision, improve their predictive capabilities to aid in better resource allocation, and ultimately reduce HRGCs crashes. [ABSTRACT FROM AUTHOR]

Excessive crosstie wear and abrasion and ballast wear and fouling are fundamental problems contributing to inadequate railroad track performance. This adversely affects the attainment and long-term maintenance of desired track geometric requirements. The magnitudes and distribution of the stresses at the crosstie-ballast (CT-B) interface must be known to determine the stress distribution on and within the ballast. However, the track design recommendations to determine these pressures, which are largely based on a methodology from the 1980s, are currently valid for modern-day railroad applications for multiple reasons discussed in this study. This study analyzed CT-B interfacial pressure data measured on an active freight mainline in Mascot, Tennessee. Dynamic contact pressures at the CT-B interface were measured using hydraulic earth pressure cells for various wheel loads and train speeds. The test train was a Federal Railroad Administration (FRA) test train consisting of a diesel electric locomotive, a test car that had different wheel loads based on the deployable axle load, and an inspection car. Although the maximum train speed was limited to 64 km=h, this research found that speed variation has a minimal effect on the CT-B interfacial pressures. From the measured data, a Gaussian stress distribution equation is proposed to determine longitudinal pressure distribution transmitted to the CT-B interface for static conditions. In addition, the stress distribution along the length of a crosstie was investigated via laboratory experimentation using a half-length crosstie. As a result of the experimentation, a dimensionless trilinear approximation was developed to estimate the stress distribution along the length of the crosstie. In general, this research recommends that the longitudinal and lateral stress distributions be considered together to design a better railroad track. [ABSTRACT FROM AUTHOR]

This paper aims to detect railway vehicle wheel flats and polygonized wheels using an envelope spectrum analysis. First, a brief explanation of railway vehicle wheel problems is presented, focusing particularly on wheel flats and polygonal wheels. Then, three types of wheel flat profiles and three periodic out-of-roundness (OOR) harmonic order ranges for the polygonal wheels are evaluated in the simulations, along with analyses implemented using only healthy wheels for comparison. Moreover, the simulation implements track irregularity profiles modelled based on the US Federal Railroad Administration (FRA). From the numerical calculations, the dynamic responses of several strain gauges (SGs) and accelerometer sensors located on the rail between sleepers are evaluated. Regarding defective wheels, only the right wheel of the first wheelset is considered as a defective wheel, but the detection methodology works for various damaged wheels located in any position. The results from the application of the methodology show that the envelope spectrum analysis successfully distinguishes a healthy wheel from a defective one. [ABSTRACT FROM AUTHOR]

This study was carried out to find the forces acting on a Siemens Velaro D type high-speed train hollow power axle commissioned by Türkiye Cumhuriyeti Devlet Demiryolları (TCDD) to examine the dynamic loads created by these forces and to understand whether these shafts are safe in terms of continued strength. Track shift forces were found for the high-speed train, and according to these forces the net axle force ratio (L/V) was calculated depending on the speed increase. These data were compared with the experimental results of the Federal Railroad Administration (American FRA) and German Railways. In this study, after determining the compatibility of the calculated results with the experimental data, various analyses such as different road conditions, load on the axle for full vehicles, variation of vertical load, dynamic factor, safety factor and continuous strength calculation for the most dangerous section were carried out. Considering these analyses, while the static load is 148 kN in the vehicle loaded condition, this load increases to 153 kN in the vehicle loaded condition and at a speed of 70 m/s. For this reason, it would be wrong to use the static load created by the weight of the vehicle alone in the classical continuous strength calculation. The dynamic load created by the vehicle travel speed must be added to the calculations. [ABSTRACT FROM AUTHOR]